Physics informed neural networks for control oriented thermal modeling of buildings

Citations

Cited by:

1. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
2. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
3. Chen, Chao & Zhang, Limao & Zhou, Cheng & Luo, Yongqiang, 2025. "Physics-informed explainable encoder-decoder deep learning for predictive estimation of building carbon emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
4. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
5. Chen, Dong & Chui, Chee-Kong & Lee, Poh Seng, 2025. "Adaptive physically consistent neural networks for data center thermal dynamics modeling," Applied Energy, Elsevier, vol. 377(PD).
6. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
7. Viorica Rozina Chifu & Tudor Cioara & Cristina Bianca Pop & Ionut Anghel & Andrei Pelle, 2024. "Physics-Informed Neural Networks for Heat Pump Load Prediction," Energies, MDPI, vol. 18(1), pages 1-20, December.
8. Pandiyan, Surya Venkatesh & Gros, Sebastien & Rajasekharan, Jayaprakash, 2025. "Physics informed neural network based multi-zone electric water heater modeling for demand response," Applied Energy, Elsevier, vol. 380(C).
9. Chen, Dongyu & Lin, Xiaojie & Qiao, Yiyuan, 2025. "Perspectives for artificial intelligence in sustainable energy systems," Energy, Elsevier, vol. 318(C).
10. Taboga, Vincent & Gehring, Clement & Cam, Mathieu Le & Dagdougui, Hanane & Bacon, Pierre-Luc, 2024. "Neural differential equations for temperature control in buildings under demand response programs," Applied Energy, Elsevier, vol. 368(C).
11. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
12. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
13. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).
14. Saloux, Etienne & Candanedo, José A. & Vallianos, Charalampos & Morovat, Navid & Zhang, Kun, 2025. "From theory to practice: A critical review of model predictive control field implementations in the built environment," Applied Energy, Elsevier, vol. 393(C).
15. Mukun Yuan & Jian Liu & Zheyuan Chen & Qingda Guo & Mingzhe Yuan & Jian Li & Guangping Yu, 2024. "Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE," Sustainability, MDPI, vol. 16(17), pages 1-27, August.
16. Tang, Lingfeng & Xie, Haipeng & Wang, Yongguan & Xu, Zhanbo, 2025. "Deeply flexible commercial building HVAC system control: A physics-aware deep learning-embedded MPC approach," Applied Energy, Elsevier, vol. 388(C).
17. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
18. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
19. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
20. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
21. Rickard Brännvall & Jonas Gustafsson & Fredrik Sandin, 2023. "Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers," Energies, MDPI, vol. 16(5), pages 1-24, February.
22. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
23. Mu, Junjin & Yang, Chunjie & Yan, Feng & Wu, Yutong & Wang, Shaoqi & Zhao, Yuchen & Yan, Duojin, 2025. "Complex coupling representation in low-dimensional space for control-oriented energy-consuming industries modeling," Applied Energy, Elsevier, vol. 383(C).
24. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).
25. Xinyue Xu & Julian Wang, 2025. "Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems," Forecasting, MDPI, vol. 7(1), pages 1-21, February.
26. Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.